Researchers have developed a new framework for estimating gradients in parameterised quantum circuits, aiming to reduce the high measurement costs associated with training. This method, based on the forward mode of automatic differentiation, uses random directional derivatives to provide an unbiased gradient estimator. The proposed QUIVER optimizer, derived from this framework, demonstrates significant efficiency gains in training quantum neural networks and outperforms existing methods on various optimization problems. AI
IMPACT Reduces computational overhead for training quantum models, potentially accelerating research and development in quantum machine learning.
RANK_REASON Academic paper detailing a new method for training quantum circuits. [lever_c_demoted from research: ic=1 ai=1.0]
- ECG5000 dataset
- gCANS
- Parameterised Quantum Circuits (PQCs)
- parameter-shift rule
- Quantum Approximate Optimisation Algorithm (QAOA)
- QUIVER
- random coordinate descent
- Variational Quantum Eigensolver (VQE)
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